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Associative classification on spatio-temporal sequences

Niccolo' Spagnuolo

Associative classification on spatio-temporal sequences.

Rel. Paolo Garza. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2021

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The main purpose of the study is to build a system to perform associative classification on spatio-temporal sequences. The proposed methodology is composed of four ordered phases: preprocessing, frequent itemsets mining, association rules generation and prediction model training. The model presented is eventually compared to other state-of-the-art classification algorithms such as Decision Trees, Random Forests and Support Vector Machines. On balance, the prediction model achieves a higher precision for the critical and most rare class with respect to its competitors.

Relators: Paolo Garza
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 59
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/19302
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